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Use a small board, make sure everything is working on a small board. You could do a Monte Carlo to decide in the next years, is an asteroid going to collide with the Earth. Indeed, people do risk management using Monte Carlo, management of what's the case of getting a year flood or a year hurricane. So we make all those moves and now, here's the unexpected finding by these people examining Go. You're going to do this quite simply, your evaluation function is merely run your Monte Carlo as many times as you can. Given how efficient you write your algorithm and how fast your computer hardware is. That's the answer. Because once somebody has made a path from their two sides, they've also created a block. Once having a position on the board, all the squares end up being unique in relation to pieces being placed on the board. We manufacture a probability by calling double probability. You can actually get probabilities out of the standard library as well. Now you could get fancy and you could assume that really some of these moves are quite similar to each other. That's what you expect. So it's really only in the first move that you could use some mathematical properties of symmetry to say that this move and that move are the same. I have to watch why do I have to be recall why I need to be in the double domain. So you can use it heavily in investment. So it's a very useful technique. You're not going to have to know anything else. I've actually informally tried that, they have wildly different guesses. We're going to make the next 24 moves by flipping a coin. Why is that not a trivial calculation? So if I left out this, probability would always return 0.{/INSERTKEYS}{/PARAGRAPH} And then, if you get a relatively high number, you're basically saying, two idiots playing from this move. How can you turn this integer into a probability? Who have sophisticated ways to seek out bridges, blocking strategies, checking strategies in whatever game or Go masters in the Go game, territorial special patterns. You're not going to have to do a static evaluation on a leaf note where you can examine what the longest path is. Maybe that means implicitly this is a preferrable move. So black moves next and black moves at random on the board. White moves at random on the board. A small board would be much easier to debug, if you write the code, the board size should be a parameter. {PARAGRAPH}{INSERTKEYS}無料 のコースのお試し 字幕 So what does Monte Carlo bring to the table? So it can be used to measure real world events, it can be used to predict odds making. Instead, the character of the position will be revealed by having two idiots play from that position. So it's not truly random obviously to provide a large number of trials. Critically, Monte Carlo is a simulation where we make heavy use of the ability to do reasonable pseudo random number generations. And you do it again. So there's no way for the other player to somehow also make a path. And that's now going to be some assessment of that decision. And that's the insight. You'd have to know some probabilities. And we're discovering that these things are getting more likely because we're understanding more now about climate change. And that's a sophisticated calculation to decide at each move who has won. The rest of the moves should be generated on the board are going to be random. So we make every possible move on that five by five board, so we have essentially 25 places to move. And we want to examine what is a good move in the five by five board. I think we had an early stage trying to predict what the odds are of a straight flush in poker for a five handed stud, five card stud. And we'll assume that white is the player who goes first and we have those 25 positions to evaluate. And these large number of trials are the basis for predicting a future event. That's the character of the hex game. Here's our hex board, we're showing a five by five, so it's a relatively small hex board. But for the moment, let's forget the optimization because that goes away pretty quickly when there's a position on the board. So here you have a very elementary, only a few operations to fill out the board. Because that involves essentially a Dijkstra like algorithm, we've talked about that before. We've seen us doing a money color trial on dice games, on poker. And so there should be no advantage for a corner move over another corner move. And at the end of filling out the rest of the board, we know who's won the game. One idiot seems to do a lot better than the other idiot. So probabilistic trials can let us get at things and otherwise we don't have ordinary mathematics work. You'd have to know some facts and figures about the solar system. But it will be a lot easier to investigate the quality of the moves whether everything is working in their program. That's going to be how you evaluate that board. The insight is you don't need two chess grandmasters or two hex grandmasters. It's int divide. So here is a wining path at the end of this game. And indeed, when you go to write your code and hopefully I've said this already, don't use the bigger boards right off the bat. Of course, you could look it up in the table and you could calculate, it's not that hard mathematically. And in this case I use 1. All right, I have to be in the double domain because I want this to be double divide. And there should be no advantage of making a move on the upper north side versus the lower south side. So you might as well go to the end of the board, figure out who won. So you could restricted some that optimization maybe the value. This white path, white as one here. So what about Monte Carlo and hex? So it's a very trivial calculation to fill out the board randomly. You readily get abilities to estimate all sorts of things. No possible moves, no examination of alpha beta, no nothing. Okay, take a second and let's think about using random numbers again. It's not a trivial calculation to decide who has won. I'll explain it now, it's worth explaining now and repeating later. And then you can probably make an estimate that hopefully would be that very, very small likelihood that we're going to have that kind of catastrophic event. But with very little computational experience, you can readily, you don't need to know to know the probabilistic stuff. So we could stop earlier whenever this would, here you show that there's still some moves to be made, there's still some empty places. And then by examining Dijkstra's once and only once, the big calculation, you get the result. So it's not going to be hard to scale on it. Turns out you might as well fill out the board because once somebody has won, there is no way to change that result. Rand gives you an integer pseudo random number, that's what rand in the basic library does for you. So here's a way to do it. So we're not going to do just plausible moves, we're going to do all moves, so if it's 11 by 11, you have to examine positions. So for this position, let's say you do it 5, times. But I'm going to explain today why it's not worth bothering to stop an examine at each move whether somebody has won. Filling out the rest of the board doesn't matter. And if you run enough trials on five card stud, you've discovered that a straight flush is roughly one in 70, And if you tried to ask most poker players what that number was, they would probably not be familiar with. So here's a five by five board. And you're going to get some ratio, white wins over 5,, how many trials? This should be a review. And we fill out the rest of the board. And the one that wins more often intrinsically is playing from a better position. Sometimes white's going to win, sometimes black's going to win.